ONSITE OIL ANALYSIS DRIVES DOWN MAINTENANCE COSTS

In a recent plant reliability survey, 60% to 70% of industrial facilities consider oil analysis an important part of their reliability programs. Oil analysis gives a snapshot of machinery health, preventing unnecessary oil changes and assisting in predicting equipment failures. This article will take a detailed look into using data to decrease maintenance costs and increase the bottom line.

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Being able to extend oil drains or even shorten them to eliminate failures can be an easy way to reduce maintenance costs, but data must be available that allows for making those decisions. This article will address the role of key performance indicators (KPIs) in predictive maintenance, how to gather useful data that aligns with KPIs and review a few case studies where onsite labs were able to use data to take advantage of warranty periods, justify keeping assets after warranty and extend the interval between oil drains to reduce oil consumption.

Oil Analysis Introduction

Lubricant analysis is much like a blood test for humans. By trending the correct parameters like blood pressure and cholesterol, the patient gains an understanding of overall health. Deviations in those trends over time indicate that action needs to be taken. The same concept can be applied to machinery health when looking at the three key areas of oil analysis: wear, contamination and chemistry. Using data in these three areas can lower overall maintenance costs, reduce unplanned downtime and increase asset life. Within a plant setting, oil analysis is often paired with several other technologies that encompass the Condition-based Maintenance (CBM) Program. The most common technology seen is vibration analysis. Typically, vibration analysis picks up on faults just a little bit later in the failure progression process than oil analysis, which is why they are typically paired together. Infrared thermography and motor circuit analysis are also used from time to time. Pairing technologies together gives confidence in the results and helps the engineer make critical decisions (if needed) to take a machine offline or remove it from service.